Optimize AI Models with Efficient Quantization Techniques

In the fast-evolving world of machine learning, efficient model deployment is key. This course is perfect for developers, data scientists, and machine learning enthusiasts who want to ensure their AI models are not only powerful but also optimized for performance on resource-constrained devices.

Throughout this course, you will delve into the core concepts of quantization techniques, including pruning and distillation. You’ll learn about essential data types like FP32, FP16, BFloat16, and INT8, and discover how to convert FP32 to BF16 and INT8 for efficient model compression. With hands-on experience, you will implement symmetric and asymmetric quantization in Python, enabling you to create leaner models ready for deployment on mobile and IoT devices.

Understanding and applying quantization is crucial for any machine learning practitioner today. By mastering these techniques, you’ll gain the skills to reduce computational load and model size while maintaining accuracy. This course strikes the perfect balance between theoretical insights and practical applications, ensuring that by the end of your learning journey, you’ll be well-equipped to optimize and deploy AI models effectively.

What you will learn:

• Learn core quantization concepts and techniques
• Implement quantization methods in Python
• Optimize models for mobile and IoT deployment

Course Content:

• Sections: 5
• Lectures: 20
• Duration: 4 hours

Requirements:

• Basic Python knowledge is recommended
• No prior AI experience required

Who is it for?

• Beginners in machine learning interested in model optimization
• AI professionals wanting to optimize models for edge devices

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

👉 Don’t miss this coupon! – Cupón EDUFREE